🎯 AI
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Executive summary: "LLM Security 101" maps the attack surface of generative AI systems by cataloging existing defenses (alignment, filtering, guardrails) and the failure modes that enable misuse. The report organizes defenses as internal alignment techniques versus external guardrail models and examines prompt defense systems and practical limitations.
Technical details:
• Alignment techniques discussed include reward-modeling and supervised fine-tuning approaches and their residual failure modes where adversarial or ambiguous inputs can trigger undesired behavior.
• External guardrails are treated as separate model layers or middleware that mediate inputs/outputs; the document highlights cases where context leakage, chaining with RAG, or model extraction enable bypass.
• Prompt-level attacks covered include classical prompt injection, jailbreak patterns, and context-manipulation that exploit tokenization or system instruction precedence.
Analysis:
• The core observation is an architectural gap: defenses embedded in the model (alignment) and defenses external to the model (guardrails) have complementary strengths but also distinct blind spots. Open-source stacks expose model internals that simplify extraction and fine-tuned abuse; closed-source stacks rely more on external filters which can be probed and bypassed.
• Compositional pipelines such as RAG increase attack surface because retrieved context can reintroduce malicious instructions or sensitive data.
Detection (conceptual):
• Monitor anomalous prompt patterns and contextual shifts that deviate from baseline user behavior.
• Track unexpected sensitivity of outputs to small context changes, indicative of prompt injection vectors.
• Correlate unusual retrieval items in RAG pipelines with downstream harmful outputs to surface poisoning attempts.
Mitigation (conceptual):
• Combine layered defenses: model-level alignment plus external runtime guardrails and output sanitization.
• Employ adversarial testing and red-teaming focused on prompt manipulation, RAG context poisoning, and extraction probes.
Limitations:
• The document emphasizes that no single control eliminates risk; trade-offs exist between openness, functionality, and attack surface. Practical controls can reduce but not eliminate specific classes of abuse.
🔹 LLM #GenAI #AI_security #prompt_injection #RAG
🔗 Source: https://hiddenlayer.com/innovation-hub/llm-security-101-the-hidden-risks-of-genai/
